Using Precision Agriculture Tools and Improved Data Analysis for Evaluating Effects of Integrated Nutrient Management Programs
Integrated nutrient management (INM) practices are becoming common under intensive agricultural systems in Chile. Practices include, the use of organic matter, in different sources, soil microbial inoculants, and the application of biostimulants, of different origin. Compared to the application of macronutrients, for example, the effects of these products on crops are rather modest and require lower experimental errors to be proven; besides, trials made at the field level, many times do not have true replications, and assignment of treatments is not random. Because of these reasons, most commonly, treatments effects cannot be proven, even though, visually, differences could be observed. To deal with this reality, precision agriculture tools and proper statistical techniques, usually those used in econometrics, that simulate ceteris paribus have been used. To compare different treatments, we have used regression with binary variables, controlling for ancillary variables such plant biomass and geographic position, and time, when this is relevant for the experiment. Besides we have corrected for spatial (and temporal) autocorrelation, using spatial lag or spatial autoregressive models. In all our experiments, field data was collected using systematic grid designs, with n>20 and an average intensity > 6 samples/ha. Plant vigor was estimated by NDVI using the active sensor OptRx (AgLeader Technologies) passed several times during the season. In the present work, results of several experiments in table grapes are presented. In all trials, plant biostimulants were applied and crop yield and quality were the response variable. Results have shown that the proposed methodology is useful to make better evaluations of field trials for INM practices and can be an excellent tool for companies wanting to evaluate their products at farmer´s fields.